Real-time detection method for liquid level based on distributed optical fiber acoustic sensing system

Abstract

The application relates to a dynamic liquid level real-time detection method based on a distributed optical fiber acoustic sensing system. The method comprises the following steps: acquiring a distributed acoustic wave sensing signal collected by a sensing optical fiber arranged in an oil casing annulus, wherein the distributed acoustic wave sensing signal is caused by a sound source and reflects the vibration response of the optical fiber in the oil casing annulus medium; based on the distributed acoustic wave sensing signal, signal amplitude characteristic data distributed along the sensing optical fiber is generated; according to the difference between the signal amplitude characteristic data in the gas phase section and the liquid phase section, the optical fiber position corresponding to the gas-liquid interface is determined, and the position is output as the dynamic liquid level position. By adopting the method, the measurement cost can be reduced, the reliability can be improved, the anti-interference capability can be enhanced, and continuous monitoring of the dynamic liquid level can be realized.

Description

Technical Field

[0001] This application relates to the field of oil and gas extraction monitoring technology, and in particular to a method for real-time detection of dynamic liquid level based on a distributed fiber optic acoustic sensing system. Background Technology

[0002] In oilfield development, dynamic fluid level depth is a key dynamic parameter reflecting formation fluid supply capacity, assessing pumping system efficiency, and optimizing production processes. Accurate and continuous monitoring of fluid level position is crucial for determining bottomhole flowing pressure, calculating submersion, developing appropriate operating procedures, and implementing intelligent extraction strategies.

[0003] Currently, commonly used methods for detecting dynamic liquid levels in the field mainly include acoustic echo method, weighing method, and nitrogen injection method. For example, the acoustic echo method emits acoustic pulses into the annulus and receives the echo signals reflected by the liquid surface and oil pipe couplings, calculating the liquid level depth based on the sound wave propagation time. Although these methods each have their own characteristics, they generally rely on dedicated hardware, resulting in problems such as system complexity, high cost, and susceptibility to operating condition interference, making it difficult to achieve low-cost, highly robust, continuous real-time monitoring.

[0004] In recent years, distributed fiber optic sensing technology has been widely used in oil and gas wells, providing continuous, high-resolution temperature and vibration data along the wellbore. However, existing fluid level detection methods have not fully utilized the structural and fluid interface information contained in these high-dimensional sensor data, and in particular, there is a lack of a method that can achieve real-time measurement of fluid level position without additional hardware, relying solely on conventional logging fiber optic data. Therefore, how to achieve an accurate, efficient, and low-cost real-time monitoring method for dynamic fluid levels has become a technical problem that needs to be solved in this field. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for real-time detection of dynamic liquid surface based on a distributed fiber optic acoustic sensing system to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for real-time detection of dynamic liquid levels based on a distributed fiber optic acoustic sensing system, comprising:

[0007] Acquire distributed acoustic wave sensing signals collected by sensing optical fibers deployed in the annulus of the oil casing and pipe. The distributed acoustic wave sensing signals are caused by a sound source and reflect the vibration response of the optical fibers in the annulus medium of the oil casing and pipe.

[0008] Based on the distributed acoustic wave sensing signal, signal amplitude characteristic data distributed along the sensing optical fiber is generated.

[0009] Based on the difference in signal amplitude characteristic data between the gas phase and liquid phase, the optical fiber position corresponding to the gas-liquid interface is determined, and this position is output as the position of the dynamic liquid surface.

[0010] In one embodiment, determining the fiber location corresponding to the gas-liquid interface based on the difference in signal amplitude characteristic data between the gas phase and liquid phase segments includes:

[0011] Identify the location where the amplitude value of the signal amplitude feature data abruptly changes from high to low along the length of the optical fiber;

[0012] The location of the amplitude abrupt change is determined to be the optical fiber location corresponding to the gas-liquid interface.

[0013] In one embodiment, identifying the location where the amplitude value of the signal amplitude feature data abruptly changes from high to low along the fiber length includes:

[0014] Calculate the first-order difference data of the signal amplitude feature data;

[0015] The position of the first negative threshold in the first-order difference data is determined as the position where the mutation occurs.

[0016] In one embodiment, before generating the signal amplitude feature data, the method further includes:

[0017] The distributed acoustic wave sensing signal is filtered to extract the frequency band signal related to the vibration characteristics of the sound source.

[0018] In one embodiment, generating signal amplitude characteristic data distributed along the sensing fiber includes:

[0019] Calculate the signal amplitude value corresponding to each sensing unit in the distributed acoustic wave sensing signal;

[0020] Based on the fiber position corresponding to each sensing unit, the signal amplitude value is displayed to form amplitude-depth distribution data. The amplitude-depth data includes a one-dimensional amplitude-depth data sequence or a two-dimensional data set composed of multiple amplitude-depth data sequences stacked together.

[0021] The amplitude-depth distribution data is smoothed to generate the signal amplitude feature data.

[0022] In one embodiment, the sound source includes a surface or underground operational sound source and a surface or underground deployment sound source; the operational sound source includes mechanical vibrations generated during the operation of well site equipment, the well site equipment includes at least one of pumping unit, electric submersible pump, and drilling equipment, and the deployment sound source is a sound source specially deployed to cooperate with real-time detection of dynamic fluid level.

[0023] In one embodiment, the distributed fiber optic acoustic sensing system includes a distributed fiber optic sensing cable, an acoustic sensing modulator / demodulator, and a real-time signal processing device.

[0024] In one embodiment, the distributed optical fiber sensing cable is deployed on the outer wall of the oil pipe, the inner wall of the casing, or the annular space between the oil pipe and the casing.

[0025] Secondly, this application also provides a real-time dynamic liquid level detection device based on distributed fiber optic acoustic sensing, comprising:

[0026] The data acquisition module is used to acquire distributed acoustic wave sensing signals collected by the sensing optical fiber deployed in the annulus of the casing pipe. The distributed acoustic wave sensing signals are caused by a sound source and reflect the vibration response of the optical fiber in the annulus medium of the casing pipe.

[0027] The feature extraction module is used to generate signal amplitude feature data distributed along the sensing optical fiber based on the distributed acoustic wave sensing signal.

[0028] The liquid level positioning module is used to determine the optical fiber position corresponding to the gas-liquid interface based on the difference in the signal amplitude characteristic data between the gas phase section and the liquid phase section, and output the position as the dynamic liquid level position.

[0029] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0030] The above-mentioned method for real-time detection of dynamic fluid level based on a distributed fiber optic acoustic sensing system can achieve accurate, continuous, and real-time detection of the fluid level position in the annulus of the casing and tubing without adding additional hardware equipment, using only the distributed fiber optic acoustic sensing data already acquired in conventional well logging and relying on the anti-interference capability of fiber optic sensing data. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is an application environment diagram of the real-time dynamic liquid level detection method in one embodiment;

[0033] Figure 2 This is a flowchart illustrating a real-time dynamic liquid level detection method in one embodiment;

[0034] Figure 3This is a flowchart illustrating the steps for generating signal amplitude characteristic data distributed along a sensing fiber in one embodiment.

[0035] Figure 4 This is a schematic diagram of one-dimensional and two-dimensional amplitude-depth data in another embodiment;

[0036] Figure 5 This is a structural block diagram of a real-time dynamic liquid level detection device in one embodiment;

[0037] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] The real-time dynamic liquid level detection method based on a distributed fiber optic acoustic sensing system provided in this application can be applied to, for example... Figure 1 In the application environment shown, the distributed fiber optic sensing system includes a distributed fiber optic sensing cable 101, an optical signal acquisition device 102, an acoustic sensing modulator / demodulator 103, and a real-time signal processing device 104. The system communicates with surface equipment outside the wellhead device 105 via the distributed fiber optic sensing cable 101. The distributed fiber optic sensing cable 101 can be installed on the outer wall of the tubing 106, the inner wall of the casing 107, or in the annular space between the tubing 106 and the casing 107 within the oil and gas well. The optical signal acquisition device 102 is responsible for emitting probe light and receiving the sensing signals returned from the fiber optic cable. The acoustic sensing modulator / demodulator 103 analyzes the monitoring data using algorithms, and the real-time signal processing device 104 performs real-time analysis of the state of the downhole oil and gas reservoir 108. The distributed fiber optic sensing system can be a standalone physical device, a cluster of multiple sensing signal demodulation modules, a distributed system, or an intelligent demodulation platform integrating data acquisition, analysis, and early warning functions.

[0040] In one exemplary embodiment, such as Figure 2 As shown, a method for real-time detection of dynamic liquid level based on a distributed fiber optic acoustic sensing system is provided, which is then applied to... Figure 1 The following steps are used as an example of the real-time signal processing device 104:

[0041] Step S201: Acquire the distributed acoustic wave sensing signal collected by the sensing fiber.

[0042] The dynamic fluid level is the dynamically changing fluid level within the annulus between the tubing and casing during normal production in an oil well. Its depth is a key dynamic parameter reflecting whether the formation's fluid supply capacity matches the operating regime of the pumping equipment. The annulus refers to the ring-shaped space between the tubing and the outer casing in an oil well. It is the sensing unit for the presence of downhole fluids (such as crude oil, natural gas, and water) and gases, and it is also the location of the dynamic fluid level.

[0043] Distributed fiber optic sensing technology utilizes optical fibers as both sensing elements and transmission media to continuously and in real-time acquire spatial distribution information of measured quantities (such as temperature, vibration, and strain) along the fiber. Distributed acoustic sensing (DAS), as a type of distributed fiber optic sensing technology, treats the optical fiber as a continuously distributed array, capturing vibration or acoustic signals along the fiber, and can be used to monitor fluid flow, vibration, etc.

[0044] The principle behind this scheme lies in the damping effect in fluid mechanics. Sound waves are mechanical waves that induce microscopic vibrations in optical fibers when propagating through a medium. When the optical fiber is immersed in a gas (such as air or natural gas), the gas has low density and low viscosity, resulting in weak damping of the fiber's vibration. Therefore, the fiber can freely follow the sound wave's vibration, and the large phase change detected by the DAS system is reflected in the signal as a high sound wave amplitude. Conversely, when the optical fiber is immersed in a liquid (such as water or crude oil), the liquid has high density and high viscosity, resulting in extremely strong damping of the fiber's vibration. This significantly limits the fiber's vibration amplitude, leading to a significant reduction in the signal amplitude detected by the DAS. This abrupt change in signal amplitude at the gas-liquid interface is the physical basis for this scheme's liquid surface positioning capability.

[0045] Specifically, this embodiment uses standard single-mode optical fiber as the sensing medium. During oil well operations, the optical fiber is lowered into the well along with the tubing via an armored optical cable, ensuring that the fiber remains vertical and taut within the annulus between the tubing and casing, in direct contact with the fluid (gas and liquid) within the annulus. Under the influence of a sound source, real-time vibration data of the optical fiber is acquired by an optical signal acquisition device, and the data is transmitted to a real-time signal processing device via an acoustic sensor modulator / demodulator.

[0046] It should be noted that the distributed acoustic sensing signal in this embodiment is caused by a sound source and reflects the vibration response of the optical fiber in the annular medium. The sound source includes operational sound sources on the ground or underground, and deployed sound sources on the ground or underground, which provide external vibration excitation. Operational sound sources include mechanical vibrations generated by well site equipment during operation or active sound source excitation applied manually. Well site equipment includes at least one of pumping units, electric submersible pumps, and drilling equipment. Deployed sound sources are specially deployed sound sources to assist in real-time detection of the dynamic fluid surface. In other words, the vibration signal in this scheme does not depend on the vibration information of the fluid surface itself.

[0047] Step S202: Based on the distributed acoustic wave sensing signal, generate signal amplitude characteristic data distributed along the sensing optical fiber.

[0048] Specifically, the sensing fiber has a series of detection points along its length, called sensing units. The data from each sensing unit corresponds to the vibration response of a different fiber segment. The time-domain signals of each sensing unit, arranged sequentially according to fiber position, form a depth sequence characterizing the amplitude of the sound wave and its distribution along the well depth. This sequence visually demonstrates the spatial variation of fiber vibration intensity from the wellhead to the bottom of the well.

[0049] Among them, the signal amplitude characteristic data can be real-time signals or statistical quantities that calculate the signal amplitude within a relatively short time window, such as root mean square amplitude, mean absolute amplitude, etc.

[0050] Step S203: Based on the difference in signal amplitude characteristic data between the gas phase and liquid phase, determine the optical fiber position corresponding to the gas-liquid interface, and output this position as the position of the moving liquid surface.

[0051] Because the damping effect of gas on fiber optic vibration is much less than that of liquid, the data values ​​corresponding to the annular air phase will remain stably in a high-level range, while the data values ​​corresponding to the liquid phase will remain stably in a low-level range in the generated signal amplitude characteristic data. At the gas-liquid interface, the data values ​​will exhibit a sharp drop. By calculating the coordinates of this drop point on the fiber optic cable, the position of the dynamic fluid level on the fiber optic cable can be obtained. Converting the fiber optic position coordinates into actual downhole depth values ​​and outputting them yields the real-time detection result of the dynamic fluid level depth.

[0052] The above method utilizes the damping effect of optical fiber to collect optical fiber vibration signals induced by ground sound sources and extracts the amplitude characteristics of the vibration signals. Based on the abrupt changes exhibited by these characteristics, the position of the dynamic liquid surface is directly located. This method does not rely on dedicated hardware, simplifying the system structure. It also improves the measurement accuracy by utilizing the anti-interference capability of optical fiber data and solves the problem that traditional methods cannot perform real-time continuous measurements through the continuous sampling capability of optical fiber sensing.

[0053] In an exemplary embodiment, after obtaining the distributed acoustic wave sensing signal in step S201, the original signal can be filtered to improve the signal-to-noise ratio and highlight the effective excitation frequency band.

[0054] It is understandable that the energy in the original DAS signal is mainly generated by a sound source, which may include different specific frequency ranges or vibrational components. For example, if the sound source is a periodically operating oil pump, its main frequency and harmonics of mechanical vibration are usually located in the lower frequency band of 10 Hz to 200 Hz; if the sound source is a man-made pneumatic horn, the sound wave energy emitted may be concentrated in the frequency band of 300 Hz to 1500 Hz or higher. Therefore, depending on the sound source, a filter frequency band can be selected specifically to allow the signal components within that frequency band to pass through without distortion or with low distortion, while filtering out high-frequency noise components, low-frequency drift components, or other vibrational interference outside that frequency band.

[0055] The above filtering process can be implemented using digital signal processing algorithms. In practice, it can be performed by the digital signal processing unit integrated within the DAS demodulator, or by the back-end real-time signal processing equipment after receiving the data stream.

[0056] The above filtering can reduce the impact of random noise and interference on subsequent signal analysis, thereby improving the accuracy of the measurement.

[0057] In one exemplary embodiment, such as Figure 3 As shown, generating signal amplitude characteristic data distributed along the sensing fiber can specifically include the following steps:

[0058] Step S301: Calculate the signal amplitude value corresponding to each sensing unit in the distributed acoustic wave sensing signal.

[0059] Step S302: Based on the fiber position corresponding to each sensing unit, the signal amplitude value is displayed to form amplitude-depth distribution data.

[0060] The amplitude-depth data includes a one-dimensional amplitude-depth data sequence or a two-dimensional data set composed of multiple stacked amplitude-depth data sequences. The amplitude-depth distribution data is a data set composed of the amplitude values ​​of all sensing units sorted according to their corresponding fiber positions. Amplitude-depth distribution data can be represented in two main forms: a one-dimensional amplitude-depth data sequence and a two-dimensional data set composed of multiple stacked amplitude-depth data sequences.

[0061] A one-dimensional amplitude-depth data sequence corresponds to a single measurement or the average result over a time slice. It can be represented as a one-dimensional array, such as array A[z], where the index z represents the depth and the array value A[z] represents the amplitude at that depth.

[0062] A two-dimensional data set, composed of multiple stacked amplitude-depth data sequences, corresponds to a set of measurement results from multiple consecutive time slices. It can be represented as a matrix, such as matrix A[z, t], where z is the depth dimension and t is the time dimension. This type of data format can simultaneously present the spatial information of the liquid surface position and its change over time, and can be used for trend analysis, spatiotemporal joint filtering, or generating monitoring pseudo-color maps.

[0063] Step S303: Smooth the amplitude-depth distribution data to generate signal amplitude feature data.

[0064] The data obtained in step S302 is smoothed to eliminate local fluctuations caused by random noise or transient interference, and to retain or enhance the effective components of the data. Smoothing can be implemented using various algorithms, including moving average smoothing, Savitzky-Golay filtering, and low-pass filtering.

[0065] In one specific embodiment Figure 4 (a) shows a one-dimensional amplitude-depth data sequence, which is displayed as a curve with the amplitude of the acoustic signal on the horizontal axis and the depth of the well on the vertical axis. Figure 4 (b) in the image represents a two-dimensional amplitude-depth data sequence composed of multiple stacked one-dimensional curves over a period of time, displayed as a pseudo-color image. The horizontal axis represents time in seconds, the vertical axis represents well depth in meters, and the color coordinates represent the amplitude of the acoustic signal; darker colors indicate higher amplitudes. The image clearly shows that the horizontal lines represent abrupt change points, allowing the approximate location of the dynamic fluid level to be determined nearby. Precise determination of the fluid level can be achieved through subsequent signal analysis.

[0066] In this embodiment, the signal processing of the original DAS signal reduces the noise component in the signal, thereby improving the stability and accuracy of the subsequent interface positioning algorithm.

[0067] In an exemplary embodiment, based on identifying the location where the amplitude value of the signal amplitude feature data abruptly changes from high to low along the fiber length direction, the specific steps include:

[0068] Step S401: Calculate the first-order difference data of the signal amplitude characteristic data.

[0069] First-order difference data, or gradient data, is used to quantify the change between adjacent data points. In the internal sections corresponding to the gas or liquid phase, the signal amplitude is relatively stable, so the first-order difference will approach zero; while at the gas-liquid interface, the signal amplitude drops sharply, and the first-order difference will produce a significant negative peak, which is the extreme point of the rate of change.

[0070] Step S402: The first position in the first-order difference data that is lower than the preset negative threshold is determined as the position where the mutation occurs.

[0071] The difference values ​​corresponding to each depth index in the first-order difference data are sequentially scanned. When an index that meets the condition is encountered for the first time, the index position is the position where the mutation occurs.

[0072] The following describes in detail the method for real-time detection of dynamic liquid surface based on distributed fiber optic acoustic sensing of this application, with reference to two specific embodiments.

[0073] Example 1 is a dynamic fluid level detection based on the environmental noise of a conventional pumping unit, which demonstrates an implementation method that uses the vibration of the inherent equipment at the well site as an excitation source to achieve low-cost continuous detection.

[0074] Step S501, System Deployment and Parameter Configuration.

[0075] Specifically, the sensing fiber uses standard single-mode communication fiber. During well operations, the fiber is placed in the annulus between the tubing and casing without additional fixation, allowing it to hang freely from the wellhead to a target depth of 1500 meters. The fiber is led out at the wellhead via a high-pressure sealing device. The led-out fiber is connected to a high spatial resolution DAS demodulator. The DAS demodulator parameters are set to a spatial resolution of 1 meter, a detection range of 0-2000 meters, and a sampling frequency of 1000 Hz. The sound source utilizes the periodic mechanical vibrations generated during the normal operation of a conventional pumping unit at the well site as a natural sound source.

[0076] Step S502, signal data acquisition.

[0077] Continuously collect raw DAS data for 10 seconds.

[0078] Step S503, signal processing and feature extraction.

[0079] The acquired raw data is first subjected to a 10-200 Hz bandpass filter to remove high-frequency noise and low-frequency drift. Next, the mean value of the filtered data from each sensor unit is calculated over 10 seconds, and the values ​​of each sensor unit are arranged in order of their corresponding fiber depth to form an amplitude-depth curve. This curve is then spatially smoothed using a moving average method with a window size of 5 meters to suppress random spatial noise and generate signal amplitude characteristic data.

[0080] Step S504: Determine the dynamic liquid level.

[0081] Calculate the first difference of the smoothed curve, set the gradient threshold to -2 / meter, find the point where the gradient first falls below the threshold at 200m, and record the position of this point, which is the position of the liquid surface.

[0082] The above embodiments make full use of existing vibration sources in the production site, eliminating the need for any special sound-generating equipment and reducing measurement costs. By filtering and spatiotemporally smoothing the vibration signal, stable amplitude spatial features are effectively extracted, and precise positioning of the liquid surface is achieved through a simple gradient threshold method.

[0083] Example 2 is based on active sound source excitation. By applying an active sound source with known characteristics, it achieves high signal-to-noise ratio and anti-interference accurate measurement, which is especially suitable for scenarios with complex background noise or requiring high-precision diagnosis.

[0084] Step S601, System Deployment and Parameter Configuration.

[0085] Specifically, the sensing fiber uses standard single-mode communication fiber. The fiber is laid by tightly securing it to the inner wall of the casing using clamps, extending from the wellhead to the target depth of 2000 meters, and then sealing it at the wellhead before exiting. The exited fiber is connected to a high spatial resolution DAS demodulator. The sound source is located at the wellhead platform, using a standard industrial pneumatic horn (frequency approximately 500Hz-2kHz) as the active sound source, emitting sound towards the annular wellhead for 30 seconds. The DAS demodulator parameters are set according to monitoring requirements to ensure coverage of the frequency range of the active sound source.

[0086] Step S602, signal data acquisition.

[0087] Turn on the active sound source and continuously collect DAS data within 30 seconds of the active sound source emitting sound.

[0088] Step S603, signal processing and feature extraction.

[0089] For the data from each sensing unit, a bandpass filter of 300-1500Hz is applied to highlight the frequency components of the active sound source. The acquired amplitude-depth curves are stacked into a two-dimensional matrix A of 2000×30, and a Gaussian smoothing kernel C of size 10×10 is selected to smooth A to obtain A1.

[0090] Step S604: Determine the dynamic liquid level.

[0091] Calculate the gradient of A1 along the depth direction and set the gradient threshold to -3 / m. The array Y containing the first position where the gradient is less than the threshold is: [150.39,149.18,148.63,149.69,151.59,149.60,150.22,149.40,147.94,150.00,151.69,149.66,148.84,149.00,150.36,149.57,150.98,149.72,147.91,149.56,150.71,151.02,151.28,151.00,151.07,151.89,151.53,150.17,149.52,149.72]. The array Y represents the dynamic change trajectory of the dynamic liquid level depth over 30 seconds.

[0092] The above embodiments, by introducing known active sound sources, can obtain sensor signals with a high signal-to-noise ratio. Employing spatiotemporal two-dimensional processing technology, the liquid surface position can be accurately determined, and the fluctuations of the liquid surface over a short period can be captured, providing richer downhole information.

[0093] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0094] Based on the same inventive concept, this application also provides a real-time dynamic liquid level detection device based on distributed fiber optic acoustic sensing for implementing the above-mentioned method. The solution provided by this device is similar to the implementation described in the above-described method. Therefore, the specific limitations of one or more embodiments of the real-time dynamic liquid level detection device based on distributed fiber optic acoustic sensing provided below can be found in the limitations of the method above, and will not be repeated here.

[0095] In one exemplary embodiment, such as Figure 5 As shown, a real-time dynamic liquid level detection device 700 based on distributed fiber optic acoustic sensing is provided, comprising:

[0096] The data acquisition module 701 is used to acquire the distributed acoustic wave sensing signal collected by the sensing optical fiber deployed in the annulus of the oil casing. The distributed acoustic wave sensing signal is caused by a sound source and reflects the vibration response of the optical fiber in the annulus medium of the oil casing.

[0097] Feature extraction module 702 is used to generate signal amplitude feature data distributed along the sensing optical fiber based on the distributed acoustic wave sensing signal;

[0098] The liquid level positioning module 703 is used to determine the optical fiber position corresponding to the gas-liquid interface based on the difference between the signal amplitude characteristic data in the gas phase section and the liquid phase section, and output the position as the dynamic liquid level position.

[0099] Each module in the aforementioned real-time dynamic liquid level detection device based on distributed fiber optic acoustic sensing can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0100] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for real-time detection of dynamic liquid levels based on distributed fiber optic acoustic sensing. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0101] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0104] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0105] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for real-time detection of dynamic liquid level based on a distributed fiber optic acoustic sensing system, characterized in that, The method includes: The distributed acoustic wave sensing signal is acquired by the sensing optical fiber deployed in the annulus of the casing and tubing. The distributed acoustic wave sensing signal is caused by a sound source and reflects the vibration response of the optical fiber in the annulus medium of the casing and tubing. The sound source includes a surface or underground working sound source and a surface or underground deployment sound source. The working sound source includes mechanical vibration generated by the operation of well site equipment. The well site equipment includes at least one of pumping unit, electric submersible pump, and drilling equipment. The deployment sound source is an active excitation sound source specially deployed to cooperate with real-time detection of dynamic fluid level. Based on the distributed acoustic wave sensing signal, signal amplitude characteristic data distributed along the sensing optical fiber is generated. Based on the difference in signal amplitude characteristic data between the gas phase and liquid phase, the optical fiber position corresponding to the gas-liquid interface is determined, and this position is output as the position of the dynamic liquid surface. The step of determining the optical fiber position corresponding to the gas-liquid interface based on the difference in signal amplitude characteristic data between the gas phase and liquid phase segments includes: Identify the location where the amplitude value of the signal amplitude feature data abruptly changes from high to low along the length of the optical fiber; The location where the amplitude changes abruptly is determined as the optical fiber location corresponding to the gas-liquid interface.

2. The method according to claim 1, characterized in that, The locations where the amplitude value of the signal amplitude feature data abruptly changes from high to low along the fiber length include: Calculate the first-order difference data of the signal amplitude feature data; The position of the first negative threshold in the first-order difference data is determined as the position where the mutation occurs.

3. The method according to claim 1, characterized in that, Before generating the signal amplitude feature data, the method further includes: The distributed acoustic wave sensing signal is filtered to extract frequency band signals related to the vibration characteristics of the sound source.

4. The method according to claim 1, characterized in that, The generated signal amplitude characteristic data distributed along the sensing optical fiber includes: Calculate the signal amplitude value corresponding to each sensing unit of the sensing fiber in the distributed acoustic wave sensing signal; Based on the fiber position corresponding to each sensing unit, the signal amplitude value is displayed to form amplitude-depth distribution data. The amplitude-depth distribution data includes a one-dimensional amplitude-depth data sequence or a two-dimensional data set composed of multiple amplitude-depth data sequences stacked together. The amplitude-depth distribution data is smoothed to generate the signal amplitude feature data.

5. The method according to claim 1, characterized in that, The distributed fiber optic acoustic sensing system includes a distributed fiber optic sensing cable, an acoustic sensing modulator / demodulator, and a real-time signal processing device.

6. The method according to claim 5, characterized in that, The distributed optical fiber sensing cable is deployed on the outer wall of the oil pipe, the inner wall of the casing, or the annular space between the oil pipe and the casing.

7. A real-time dynamic liquid level detection device based on distributed fiber optic acoustic sensing, characterized in that, The device includes: The data acquisition module is used to acquire distributed acoustic wave sensing signals collected by sensing optical fibers deployed in the annulus of the casing and tubing. The distributed acoustic wave sensing signals are caused by sound sources and reflect the vibration response of the optical fibers in the annulus medium of the casing and tubing. The sound sources include ground or underground operational sound sources and ground or underground deployment sound sources. The operational sound sources include mechanical vibrations generated by well site equipment during operation. The well site equipment includes at least one of pumping units, electric submersible pumps, and drilling equipment. The deployment sound source is an actively excitation sound source specially deployed to cooperate with real-time dynamic fluid level detection. The feature extraction module is used to generate signal amplitude feature data distributed along the sensing optical fiber based on the distributed acoustic wave sensing signal. The liquid level positioning module is used to determine the optical fiber position corresponding to the gas-liquid interface based on the difference in signal amplitude characteristic data between the gas phase and liquid phase sections, and output this position as the dynamic liquid level position. Determining the optical fiber position corresponding to the gas-liquid interface based on the difference in signal amplitude characteristic data between the gas phase and liquid phase sections includes: identifying the position where the amplitude value of the signal amplitude characteristic data changes abruptly from high to low along the length of the optical fiber; and determining the position where the amplitude change occurs as the optical fiber position corresponding to the gas-liquid interface.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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